When I first wrote it “Vector Database: Shiny Object Syndrome and the Case of the Missing Unicorn” In March 2024, the industry was full of hype. vector database is next big thing — An essential infrastructure layer for the AI generation. Billions of dollars in venture funding were flowing, developers were rushing to integrate embedding into their pipelines, and analysts were tracking funding rounds with bated breath. pine cone, Weaviate, Saturation, milvus About a dozen others.
This promise was very appealing. Finally, there is a way to search by meaning rather than by obscure keywords. Simply dump your enterprise knowledge into a vector store, connect to LLM, and watch the magic happen.
However, the magic never fully materialized.
2 years later, reality check 95% of organizations that invest in AI initiatives see zero measurable returns. And many of the warnings I raised at the time – about the limitations of vectors, vendor crowding, and the risks of treating vector databases as silver bullets – played out more or less predictably.
Prophecy 1: The missing unicorn
At the time, I wondered whether Pinecone, the poster child for this category, would achieve unicorn status or become the “lost unicorn” of the database world. Today, that question was answered in the most obvious way possible. The pine cone reportedly considering sellingis struggling to survive amid fierce competition and customer loss.
Yes, Pinecone raised big money and signed the logo on the marquee. However, in reality, differentiation was weak. Open source players like Milvus, Qdrant, and Chroma undercut them in terms of cost. Existing companies like Postgres ( pg vector) and Elasticsearch simply added vector support as a feature. And customers are increasingly asking questions like: “Why introduce a completely new database when your existing stack already performs the vectors well?”
As a result, Pinecone, once worth nearly $1 billion, is now looking for a home. A truly missing unicorn. In September 2025, Pinecone appointed Ashish Ashutosh He will become CEO, and founder Ed Liberty will move to the role of chief scientist. The leadership change comes amid increasing pressure and doubts about long-term independence.
Prediction 2: Vectors alone cannot solve the problem.
He also argued that vector databases alone are not the final solution. For use cases that require precision, such as searching for “Error 221” in a manual, a pure vector search will happily show “Error 222” as “close enough.” Cute in demo, but devastating in production.
The tension between similarity and relevance proved fatal to the myth of vector databases as universal engines.
“Companies have discovered the hard way that semantics = correct.”
The developers who happily replaced lexical search in vectors quickly reintroduced… lexical search in combination with vectors. The team, hoping vectors would “work,” ended up relying on metadata filtering. Reranker and manually adjusted rules. By 2025, the consensus will be clear that vectors are powerful, but only to be used as part of a hybrid stack.
Prediction 3: Crowded fields will become commoditized
The explosion of vector database startups was never sustainable. Weaviate, Milvus (via Zilliz), Chroma, Vespa, Qdrant — each claimed subtle differentiators, but for most buyers, they all did the same thing. That is, we saved the vector and got the nearest neighbor.
There are very few breakout players right now. Markets are increasingly fragmented, commoditized, and in many ways swallowed up by incumbents. Vector search is now a checkbox feature in cloud data platforms rather than a standalone moat.
As I wrote at the time, it will become increasingly difficult to distinguish one vector DB from another. The challenge is becoming increasingly difficult. Valdo, marco, Lance DB, PostgresSQL, MySQL Heatwave, oracle 23c, Azure SQL, cassandra, ladies, Neo4j, single store, elastic search, open search, Apahce Solr…the list goes on.
New reality: Hybrid and GraphRAG
But this is not just a story of decline, but of evolution. From the ashes of vector hype is emerging a new paradigm that combines the best of multiple approaches.
Hybrid search: keyword + vector is now the default for serious applications. Companies have learned that they need both precision and ambiguity, precision and semantics. Tools like Apache Solr, Elasticsearch, pgVector, and Pinecone’s own “cascade search” employ this.
Graph RAG: The hottest buzzword for late 2024 and 2025 is GraphRAG (Graph Enhanced Search Augmented Generation). GraphRAG combines vectors and knowledge graphs to encode relationships between entities that would be flattened by embedding alone. The payoff is dramatic.
Benchmarks and evidence
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Amazon’s AI Blog I quote the benchmark from RetoriaHybrid GraphRAG improved answer accuracy from up to 50% to over 80% on test datasets spanning financial, medical, industrial, and legal.
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of GraphRAG-Bench The benchmark (released May 2025) provides a rigorous evaluation of GraphRAG and vanilla RAG across inference tasks, multihop queries, and domain challenges.
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Ann OpenReview evaluation of RAG and GraphRAG We found that each approach has its strengths depending on the task, but a hybrid combination often performs best.
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FalkorDB blog report When schema accuracy is important (structured domain), GraphRAG outperforms vector search by up to 3.4x on certain benchmarks.
The rise of GraphRAG highlights a more important point. In other words, searching is not about a single shiny object. it’s about building search system — A layered, hybrid, context-aware pipeline that provides the right information to the LLM at the right time, with the right precision.
What does this mean going forward?
Here’s the verdict: The Vector database was no miracle. These were important steps in the evolution of search and retrieval. But they are not, and never were, the final stages.
The winners in this space will not be companies that sell vectors as standalone databases. They will incorporate vector search into a broader ecosystem, integrating graphs, metadata, rules, and context engineering into a consistent platform.
In other words, Unicorn is not a vector database. Unicorn is a search stack.
Looking to the future: What happens next?
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A unified data platform incorporates vectors and graphs. We expect major DB and cloud vendors to offer a unified search stack (vector + graph + full text) as a built-in feature.
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“Search Engineering” emerges as a separate discipline. As MLOps matures, so will practices around embedded tuning, hybrid ranking, and graph construction.
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A metamodel that learns better queries: The future LLM is learn Adjust which retrieval method is used for each query and dynamically adjust the weights.
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Temporal and multimodal GraphRAG: Researchers are already extending GraphRAG to be time-aware (T-Grag) and multimodally integrated (e.g. connecting images, text, and video).
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Open benchmarks and abstraction layers: tools like Benchmark QED (for the RAG Benchmark) and GraphRAG-Bench drive the community towards a fairer and more comparable measurement system.
From shiny objects to critical infrastructure
The story of vector databases has followed the classic path of a popular hype cycle followed by introspection, revision, and maturation. In 2025, vector search is no longer a shiny object that everyone blindly chases, but a key component within a more sophisticated, multifaceted search architecture.
The initial warnings were correct. Pure vector-based hopes are often dashed by precision, relational complexity, and enterprise constraints. However, this technology was not wasted. This technology has forced the industry to rethink search by combining semantic, lexical, and relational strategies.
I suspect that if we were to write a sequel in 2027, we would configure vector databases as legacy infrastructure rather than as unicorns. Although basic, it will be treated as infrastructure overshadowed by smarter orchestration layers, adaptive search controllers, and dynamically selective AI systems. which one The search tool matches your query.
At the moment, the real battle is not about vectors and keywords. Indirection, blending, and discipline in building search pipelines that ensure generative AI is grounded in facts and domain knowledge. That is the unicorn we should be chasing now.
Amit Verma is the Head of Engineering and AI Lab. neuron 7.
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